Computer Science ›› 2020, Vol. 47 ›› Issue (11A): 78-82.doi: 10.11896/jsjkx.200400061

• Artificial Intelligence • Previous Articles     Next Articles

Sentiment Classification of Network Reviews Combining Extended Dictionary and Self-supervised Learning

JING Li, LI Man-man, HE Ting-ting   

  1. School of Computer and Information Engineering,Henan University of Economics and Law,Zhengzhou 450046,China
  • Online:2020-11-15 Published:2020-11-17
  • About author:JING Li,born in 1971,Ph.D,professor,is a member of China Computer Federation.Her main research interests include artificial intelligence and information security.
    LI Man-man,born in 1992,postgradua-te.Her main research interests include data analysis,data mining and natural language processing.
  • Supported by:
    This work was supported by the National Natural Science Foundation of China(61806073,31700858,61802110).

Abstract: In the rapidly developing Internet era,sentiment analysis of online reviews plays an important role in analyzing public opinion and monitoring e-commerce.Existing classification methods mainly include sentiment dictionary methods and machine learning methods.The sentiment dictionary method relies too much on the sentiment words in the dictionary.The more complete the sentiment dictionary,the more pronounced the sentiment tendency of online comments and the better classification effect.The classification effect of comments is not good when the sentiment tendencies are not easy to distinguish.The machine learning method is a supervised method,and its classification effect relies on a large number of pre-annotated corpora.Currently,the corpus annotation is done manually,and the workload is extremely large.This paper combines characteristics of the two methods to build a new sentiment classification model of network reviews.First,the sentiment dictionary is expanded based on the domain of online reviews,andthe sentiment value of each online comment is calculated according to the extended sentiment dictionary.According to the preset sentiment threshold,the comments with significant is sentiment tendencies and higher accuracy are selected as the definite set,and the rest that are not easily distinguished are used as uncertain sets.The classification result of the definite set is directly determined by the sentiment value.Second,according to the definite set from the sentiment dictionary method,a classifier is trained through self-supervised learning,and the training data do not require manual annotation.Finally,the trained classifier is used to classify the uncertain set again,and an improved algorithm is used to improve the classification result of the uncertain set.Experiments show that,compared with the sentiment dictionary method and the machine learning method,the proposed model achieves a better sentiment classification effect for the sentiment classification of hotel reviews and Jingdong reviews.

Key words: Internet reviews, Machine learning, Sentiment classification, Sentiment dictionary, Word vectors

CLC Number: 

  • TP391.1
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